Jobs in Palo Alto, CA

About the role

We're looking for an infrastructure engineer to design and build the core systems behind how we train our models with reinforcement learning (RL). You'll own the training infrastructure end to end, from rollout and reward pipelines to orchestration, reliability, and observability. The work spans both the algorithmic side of RL and the systems reality of running distributed training at scale, and you'll partner closely with our research team to keep RL training fast, stable, and dependable for the multimodal, visual reasoning models at the center of our work.

What you'll bring

• 3+ years of distributed systems experience, including building or optimizing large-scale RL training pipelines (PPO, GRPO, or similar on-policy methods)
• Experience with actor-learner architectures and environment rollout orchestration at scale
• Strong Python skills, plus PyTorch or JAX
• Experience with async training infrastructure, replay buffers, or simulation-based environment frameworks
• Multi-node GPU orchestration experience (Ray, SLURM, or Kubernetes)
• A track record of improving training throughput and GPU utilization at scale
• Strong engineering skills
• ability to contribute performant, maintainable code and debug in complex codebases
• Experience with multimodal or agentic RL environments
• Experience with RLHF or reward modeling pipelines
• A self-directed builder who moves quickly and works across teams in an early-stage setting

Benefits

Locations